






"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = 42



class        UpperCamelCase__ (				a_							,	a_):







    """simple docstring"""







    @register_to_config
    def __init__(		self :	Union[str, Any]				,    UpperCamelCase_ :	int = 6_5_5_3_6				,    UpperCamelCase_ :	Optional[int] = None				,    UpperCamelCase_ :	int = 2				,    UpperCamelCase_ :	int = 2				,    UpperCamelCase_ :	int = 0				,    UpperCamelCase_ :	str = "fourier"				,    UpperCamelCase_ :	bool = True				,    UpperCamelCase_ :	bool = False				,    UpperCamelCase_ :	float = 0.0				,    UpperCamelCase_ :	Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,    UpperCamelCase_ :	Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,    UpperCamelCase_ :	Tuple[str] = "UNetMidBlock1D"				,    UpperCamelCase_ :	str = None				,    UpperCamelCase_ :	Tuple[int] = (3_2, 3_2, 6_4)				,    UpperCamelCase_ :	str = None				,    UpperCamelCase_ :	int = 8				,    UpperCamelCase_ :	int = 1				,    UpperCamelCase_ :	bool = False				,    ):





           '''simple docstring'''

           super().__init__()
           __magic_name__      				=    sample_size

           # time
           if time_embedding_type == "fourier":
                  __magic_name__      				=    GaussianFourierProjection(
                      embedding_size=8				,    set_W_to_weight=UpperCamelCase_				,    log=UpperCamelCase_				,    flip_sin_to_cos=UpperCamelCase_ )
                  __magic_name__      				=    2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                  __magic_name__      				=    Timesteps(
                      block_out_channels[0]				,    flip_sin_to_cos=UpperCamelCase_				,    downscale_freq_shift=UpperCamelCase_ )
                  __magic_name__      				=    block_out_channels[0]

           if use_timestep_embedding:
                  __magic_name__      				=    block_out_channels[0] * 4
                  __magic_name__      				=    TimestepEmbedding(
                      in_channels=UpperCamelCase_				,    time_embed_dim=UpperCamelCase_				,    act_fn=UpperCamelCase_				,    out_dim=block_out_channels[0]				,    )

           __magic_name__      				=    nn.ModuleList([] )
           __magic_name__      				=    None
           __magic_name__      				=    nn.ModuleList([] )
           __magic_name__      				=    None

           # down
           __magic_name__      				=    in_channels
           for i, down_block_type in enumerate(UpperCamelCase_ ):
                  __magic_name__      				=    output_channel
                  __magic_name__      				=    block_out_channels[i]

                  if i == 0:
                         input_channel += extra_in_channels

                  __magic_name__      				=    i == len(UpperCamelCase_ ) - 1

                  __magic_name__      				=    get_down_block(
                      UpperCamelCase_				,    num_layers=UpperCamelCase_				,    in_channels=UpperCamelCase_				,    out_channels=UpperCamelCase_				,    temb_channels=block_out_channels[0]				,    add_downsample=not is_final_block or downsample_each_block				,    )
                  self.down_blocks.append(UpperCamelCase_ )

           # mid
           __magic_name__      				=    get_mid_block(
               UpperCamelCase_				,    in_channels=block_out_channels[-1]				,    mid_channels=block_out_channels[-1]				,    out_channels=block_out_channels[-1]				,    embed_dim=block_out_channels[0]				,    num_layers=UpperCamelCase_				,    add_downsample=UpperCamelCase_				,    )

           # up
           __magic_name__      				=    list(reversed(UpperCamelCase_ ) )
           __magic_name__      				=    reversed_block_out_channels[0]
           if out_block_type is None:
                  __magic_name__      				=    out_channels
           else:
                  __magic_name__      				=    block_out_channels[0]

           for i, up_block_type in enumerate(UpperCamelCase_ ):
                  __magic_name__      				=    output_channel
                  __magic_name__      				=    (
                      reversed_block_out_channels[i + 1] if i < len(UpperCamelCase_ ) - 1 else final_upsample_channels
                  )

                  __magic_name__      				=    i == len(UpperCamelCase_ ) - 1

                  __magic_name__      				=    get_up_block(
                      UpperCamelCase_				,    num_layers=UpperCamelCase_				,    in_channels=UpperCamelCase_				,    out_channels=UpperCamelCase_				,    temb_channels=block_out_channels[0]				,    add_upsample=not is_final_block				,    )
                  self.up_blocks.append(UpperCamelCase_ )
                  __magic_name__      				=    output_channel

           # out
           __magic_name__      				=    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,    3_2 )
           __magic_name__      				=    get_out_block(
               out_block_type=UpperCamelCase_				,    num_groups_out=UpperCamelCase_				,    embed_dim=block_out_channels[0]				,    out_channels=UpperCamelCase_				,    act_fn=UpperCamelCase_				,    fc_dim=block_out_channels[-1] // 4				,    )







    def 				a__			(		self :	Any				,    UpperCamelCase_ :	torch.FloatTensor				,    UpperCamelCase_ :	Union[torch.Tensor, float, int]				,    UpperCamelCase_ :	bool = True				,    ):





           '''simple docstring'''

           __magic_name__      				=    timestep
           if not torch.is_tensor(UpperCamelCase_ ):
                  __magic_name__      				=    torch.tensor([timesteps]				,    dtype=torch.long				,    device=sample.device )
           elif torch.is_tensor(UpperCamelCase_ ) and len(timesteps.shape ) == 0:
                  __magic_name__      				=    timesteps[None].to(sample.device )

           __magic_name__      				=    self.time_proj(UpperCamelCase_ )
           if self.config.use_timestep_embedding:
                  __magic_name__      				=    self.time_mlp(UpperCamelCase_ )
           else:
                  __magic_name__      				=    timestep_embed[..., None]
                  __magic_name__      				=    timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
                  __magic_name__      				=    timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

           # 2. down
           __magic_name__      				=    ()
           for downsample_block in self.down_blocks:
                  __magic_name__				,  __magic_name__      				=    downsample_block(hidden_states=UpperCamelCase_				,    temb=UpperCamelCase_ )
                  down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                  __magic_name__      				=    self.mid_block(UpperCamelCase_				,    UpperCamelCase_ )

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks ):
                  __magic_name__      				=    down_block_res_samples[-1:]
                  __magic_name__      				=    down_block_res_samples[:-1]
                  __magic_name__      				=    upsample_block(UpperCamelCase_				,    res_hidden_states_tuple=UpperCamelCase_				,    temb=UpperCamelCase_ )

           # 5. post-process
           if self.out_block:
                  __magic_name__      				=    self.out_block(UpperCamelCase_				,    UpperCamelCase_ )

           if not return_dict:
                  return (sample,)

           return UNetaDOutput(sample=UpperCamelCase_ )







"""simple docstring"""
def 							A						(				__snake_case: str  )    ->		list:

       """simple docstring"""





       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__snake_case  )  )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
 __import__("""doctest""").testmod()